Study Questions Assumed Link Between Classification and Explanation Robustness in Deep Learning
A new paper on arXiv argues that classification robustness and explanation robustness in image classification systems are not strongly correlated, contradicting a widely held assumption in deep learning research. The authors introduce a clustering-based evaluation method and a novel training approach to manipulate the loss landscape with respect to explanation loss. The findings suggest that improving one form of robustness does not reliably improve the other, with implications for how AI systems are evaluated and made trustworthy.
Researchers have published a preprint on arXiv challenging the prevailing belief that classification robustness and explanation robustness in deep learning image classifiers are inherently linked. Using a clustering-based approach for efficient evaluation of explanation robustness, the study finds that enhancing explanation robustness does not necessarily flatten the input loss landscape relative to explanation loss — a property typically associated with better classification robustness. To probe this contradiction further, the team developed a novel training method designed to directly adjust the loss landscape with respect to explanation loss. Experiments showed that while this adjustment can affect explanation robustness, it has no measurable impact on classification robustness. These results challenge a foundational assumption in the field and open new avenues for separately understanding and optimizing the two forms of robustness in AI systems.
What different sources said
- arXiv cs.LGCenter
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
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